Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury. Frontiers in Bioengineering and Biotechnology, 8. Online ahead of publication.
Drug-induced liver injury (DILI) is one of the most cited reasons for the high drug attrition rate and drug withdrawal from the market. The accumulated large amount of high throughput transcriptomic profiles and advances in deep learning provide an unprecedented opportunity to improve the suboptimal performance of DILI prediction. In this study, we developed an eight-layer Deep Neural Network (DNN) model for DILI prediction using transcriptomic profiles of human cell lines (LINCS L1000 dataset) with the current largest binary DILI annotation data [i.e., DILI severity and toxicity (DILIst)]. The developed models were evaluated by Monte Carlo cross-validation (MCCV), permutation test, and an independent validation (IV) set…
By Chloe Atkinson|2022-11-28T14:08:22+00:00June 17th, 2021|Comments Off on Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury. Frontiers in Bioengineering and Biotechnology, 8. Online ahead of publication.